CN1663263A - 用于推荐代表用户兴趣的项目的自适应原型简档的方法和设备 - Google Patents
用于推荐代表用户兴趣的项目的自适应原型简档的方法和设备 Download PDFInfo
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Abstract
公开一种用于向用户推荐感兴趣的项目的方法和设备,例如电视节目推荐。根据本发明的原理,修改或转换在得到用户的收看历史或购买历史之前生成的最初推荐,以使用反馈处理更好地捕捉用户的收看行为。特别地,生成的原型被用于建立原型简档。然后生成的原型简档反映由代表性观众选择的项目的典型模式。使用原型简档计算相对于实地真实数据的推荐,其中用原型简档中的每个原型的矩心来计算在所谓实地真实数据的每个演出之间的间距。如果计算的推荐与原始的实地真实数据之间不一致,则从用户请求附加反馈,该反馈用来创建在后简档。在后简档包括用户已经向他/她希望推荐或放弃的演出提供的所有加权(比如正/负的补充)的集合。最后,通过使用相对于原型简档的在后简档来重新计算推荐。
Description
技术领域
本发明涉及用于推荐例如电视节目等感兴趣的项目的方法和设备,尤其涉及用于推荐感兴趣的节目以及其它项目的技术。
背景技术
随着电视观众可观看的频道数以及在这些频道上可看的节目内容的多样性的增长,对于电视观众来讲,辨别感兴趣的节目已经逐渐成为一种难题。电子节目指南(EPG)通过例如标题、时间、日期和频道标识出可看的电视节目,并且通过允许根据个性化偏好搜索或分类可用的电视节目,方便了对感兴趣的节目进行识别。
已经对推荐感兴趣的电视节目和其它项目建议或提议了多种推荐工具。例如,电视节目推荐工具将观众的偏好应用到EPG以获得特定观众感兴趣的一组推荐节目。总体上,电视节目推荐工具使用隐式或显式技术,或者使用上述技术的一些组合来获得观众的偏好。隐式电视节目推荐工具以不引人注目的方式,根据从观众的收看历史得到的信息生成电视节目推荐。另一方面,显式电视节目推荐工具明确地询问观众对节目属性的偏好,诸如标题、流派、演员、频道和日期/时间之类,以得到观众简档并生成推荐。
虽然当前可用的推荐工具帮助用户识别感兴趣的项目,但是他们受到多种限制,如果克服了这些限制,可以大大提高上述推荐工具的便利性和性能。例如,为了更加全面,显式推荐工具的初始化非常冗长,要求每个新用户响应一个非常详细的调查,将他们的偏好指定在一个粗略的粒度级别上。虽然隐式电视节目推荐工具通过观察收看行为不引入注目地得到简档,但是需要长的时间使之准确。另外,这种隐式电视节目推荐工具至少需要最小量的收看历史才开始做出推荐。因此,当第一次得到这种隐式电视节目推荐工具时不能做出推荐。
因此存在这样的需要,即在得到充分的个性化收看历史之前,能够不引人注目地推荐诸如电视节目等项目的方法和设备。另外,存在对准确地捕捉用户收看行为的方法和设备的需要。
总得来说,公开了一种向用户推荐感兴趣的项目的方法和设备,例如电视节目推荐。根据本发明的原理,修改或转换在得到用户的收看历史或购买历史之前生成的最初推荐以使用反馈处理来更好地捕捉用户的收看行为。
最初,例如根据特定收看区域的收看历史生成原型(stereotype),其被用于建立原型简档。生成的原型简档反映由代表性的观众所选择的项目的典型模式。使用原型简档相对于实地真实(ground truth)数据来计算推荐。用原型简档中的每个原型的矩心计算在实地真实数据中每个演出之间的间距。如果计算的推荐与原始的实地真实数据之间不一致,则从用户请求附加的反馈,该反馈被用于创建在后简档(meta-profile)。在后简档包括用户已经向他/她希望被推荐或放弃的演出所提供的所有加权(例如正/负的补充)的集合。最后,使用相对于原型简档的在后简档重新计算推荐。
附图说明
通过参考下列详细的描述和附图,将获得对本发明的更全面的理解,以及本发明进一步的特征和优势。
图1是根据本发明的电视节目推荐器的示意框图;
图2是描述实现本发明原理的图1中自适应原型简档处理的流程图。
具体实施方式
图1图解根据本发明的电视节目推荐器100。如图1所示,示范性的电视节目推荐器100评估节目数据库200中的节目,以标识特定观众感兴趣的节目。例如,使用公知的在屏幕上显示技术的置顶终端/电视(未示出)可以向观众呈现一组推荐的节目。虽然在此是以电视节目推荐来举例说明本发明,但是本发明可应用到基于对诸如收看历史或者购买历史等用户行为的评估而自动生成的推荐。特别地,像机顶盒,TiVo之类的设备(硬盘记录器、PVR,等等)。本发明还可用于使用用户简档群集的任何应用。在全球网-简档的情况下,本发明被嵌入网络浏览器中。
在可得到用户的收看历史140之前,例如当用户第一次获得电视节目推荐器100时,电视节目推荐器100生成电视节目推荐。如图1所示,电视节目推荐器100利用来自一个或多个第三方的收看历史130向特定用户推荐感兴趣的节目。通常,第三方收看历史130基于一个或多个抽样人口的收看习惯,人口统计例如代表大多数人口的年龄、收入、性别和教育。
如图1所示,第三方收看历史130包含一组被特定人群收看或者没有收看的节目。通过观察被特定人群实际收看的节目获得被收看的那组节目。例如,通过随机抽样节目数据库200中的节目获得未被收看的那组节目。在进一步的变型中,根据2001年3月28日提交的,题为“An Adaptive Sampling Technique for Selecting NegativeExamples for Artificial Intelligence Applications”的美国专利申请序列号No.09/819,286的教导获得未被收看的那组节目。该申请已被转让给本发明的受让人并在此引入作为参考。
电视节目推荐器100处理第三方收看历史130以生成原型简档,其反映由代表性的观众收看的电视节目的典型模式。一个原型简档是在某些方面互相类似的电视节目(数据点)的群集。可以使用多种方式的任何一种生成原型简档。例如,如在2001年11月14日提交的题为“Method and Apparatus for Generating a StereotypicalProfile for Recommending Items of Interest Using Item-BasedClustering”的美国专利申请序列号NO.xx/xxx,xxx,以及2001年11月13日提交的题为“Method and Apparatus for Generating aStereotypical Profile for Recommending Items of InterestUsing Feature-Based Clustering”的美国专利申请序列号NO.xx/xxx,xxx中所描述的。每个申请在此引入作为参考。
电视节目推荐器100可以实现为任何一种计算设备,例如个人计算机或工作站等设备,它包括例如中央处理单元(CPU)的处理器115,和例如RAM和/或ROM的存储器120。例如,电视节目推荐器100还可以实现为在置顶终端或显示器(未示出)中的专用集成电路(ASIC)。另外,电视节目推荐器100可以实现为任意可用的电视节目推荐器,诸如可从加利福尼亚Sunyvale的Tivo公司得到的商用TivoTM系统,或者在1999年12月17日提交的题为“Method and Apparatus forRecommending Television Programming Using Decision Trees”的美国专利申请No.09/466,406中所描述的电视节目推荐器,以及在2000年2月4日提交,题目为“Bayesian TV Show Recommender”的美国专利申请No.09/498,271中所描述的电视节目推荐器,以及在于2000年7月27日提交的题为“Three-Way Media RecommendationMethod and System”的美国专利申请No.09/627,139中所描述的电视节目推荐器,或者他们的任意组合,每个申请都在此引入作为参考。
电视节目推荐器100包括节目数据库200和存储器120中的服务器例行程序,例如原型简档处理300,以及(未示出)群集例行程序,均值计算例行程序,间距计算例行程序和群集性能估算例行程序。通常,节目数据库200可以实现为已知的电子节目指南并记录在给定时间间隔中可获得的每个节目的信息。自适应原型简档处理300(i)处理第三方收看历史130以生成原型简档,原型简档反映了由代表性的观众收看的电视节目的典型模式;(ii)使用所选的原型生成相对于所谓实地真实情况的推荐,用原型简档中的每个原型的矩心来计算实地真实数据中的每个演出之间的间距(实地真实数据是用户已经给出特定信息的一组演出,特定信息例如是他/她喜欢那个演出的程度。例如,用户可指示他/她热爱演出“Seinfeld”,热爱可以被转换成0.85和1.0之间或者被转换为其它适合的评分换算方式);(iii)如果在计算的推荐与原始的实地真实数据之间存在不一致(例如,如果用户指示他/她热爱“Seinfeld”,则分数应当在0.85和1.0之间,因此我们知道当计算推荐时评分小于0.85,则存在不一致)。然后从用户请求的附加反馈使用用户反馈160来转换推荐;(iv)接着使用用户反馈创建一个在后简档(meta-profile),在后简档包括用户已经向他/她希望被推荐或放弃的演出所提供的所有加权(比如正/负的补充)的集合。(v)使用相对于原型简档的在后简档来重新计算推荐。
特别地,在一个示范性实施例中,群集例行程序可以被自适应原型简档处理300调用,以将第三方收看历史130(数据集合)划分为群集,以便在一个群集中的那些点(电视节目)比任何其它的群集更接近那个群集的均值(矩心)。群集例行程序调用均值计算例行程序来计算群集的符号均值。间距计算例行程序被群集例行程序调用,以基于特定电视节目与特定群集的均值之间的间距来估算电视节目与每个群集的接近度。接着群集例行程序调用群集性能估算例行程序,确定何时满足用于生成群集的停止标准,如在2001年11月13日提交的题为“Method and Apparatus for Generating a stereotypicalprofile for recommending items of interest using feature-based clustering”的美国专利申请No.10/014,189的中所进一步描述的,该申请在此引入作为参考。
图2是一个流程图,它描述了具有本发明特征的自适应原型简档处理300的示范性执行。如前面指示的,自适应原型简档处理300在步骤310处理第三方收看历史130,以根据反映由代表性观众收看的电视节目的典型模式的原型来生成原型简档。在步骤320使用选择的原型生成相对于实地真实数据的推荐。通过使用下列等式,用原型简档中的每个原型的矩心计算在实地真实数据中的每个演出之间的间距来计算推荐。
这里S1和S2对应两个演出,N对应构成演出记录的特征数目。请注意间距D被规一化为处于0和1之间。
此后在步骤330-350,计算出的推荐与原始实地真实数据相比,如果它们之间存在不一致,则用户被提示关于推荐的附加反馈。能够通过任何常规的方法从用户获得反馈。接着该反馈被用于形成加权因子。作为一个实例,如果用户指示他喜欢所有Clint Eastwood的电影,那么有Clint Eastwood演出的全部评分都被增加,反之亦然。另外,该加权因子被用在节目级别以及特征级别。例如,在整个演出级别或构成演出的诸如演员、种类等特征。在步骤360,反馈被用于创建在后简档,该简档包括用户已经为他/她希望被推荐或放弃的演出提供的所有加权(比如正/负的补充)的集合。最后,在步骤370,通过使用相对于原型简档的在后简档来重新计算推荐:
应当注意,因为简档中的演出本身是矩心,所以对原型简档的加权常常被设置成1。直观地,当用户给出反馈时,他/她希望演出的评分离矩心更近或远离矩心。应注意上面给出的措施给出了一个间距。理想地,演出具有零间距时,这意味着演出更接近矩心。为了得到一个评分;从1中减去。作为例证,如果用户已经对特定演出给出下列反馈——不关心、喜欢和热爱,这分别对应于0、0.7和1。另外,让我们假设在演出与原型简档之间实际计算的间距为0.2。下表显示用上面所示的等式计算的数值。
加权 间距 概率
...........................................
0 0.2 0.8
0.7 0.06 0.94
1 0 1.0
要注意的是,在用户完全不喜欢的情况下,需要建立特殊的边界条件,例如,如果用户说(-1),则根本不推荐这个演出。在间距超过1的情况下,间距应当被重新规一化以便可以计算分数。
应当理解,在此显示和描述的实施例和变形只是举例说明本发明的原理,并且由本领域的熟练技术人员可以做出各种修改而不脱离本发明的精神和范围。
Claims (12)
1.一种用于在推荐器中向用户推荐感兴趣的项目的方法,所述方法包括步骤:
使用原型简档和实地真实数据生成最初的推荐;
如果最初的推荐与实地真实数据不一致,获得关于推荐的用户反馈;
使用用户反馈生成修订的推荐。
2.权利要求1的方法,其中所述生成最初推荐的步骤包括生成原型,其用于建立原型简档。
3.权利要求2的方法,其中所述生成最初推荐的步骤包括用原型简档中的每个原型的矩心计算在实地真实数据的每个演出之间的间距。
4.权利要求3的方法,其中所述生成修订的推荐的步骤包括创建在后简档,所述在后简档包括一组基于用户反馈的加权因子,所述在后简档被用于生成修订的推荐。
5.权利要求3的方法,其中所述用于特定符号特征的两个值S1和S2之间的间距D由:
给出,其中S1和S2对应两个项目,N对应构成该项目的原型的数目。
6.权利要求4的方法,其中所述生成修订推荐包括通过应用相对于原型简档的在后简档W来计算修订间距D,间距D由:
给出,其中S1和S2对应两个项目,N对应构成该项目的原型的数目。
7.权利要求1的方法,其中所述项目是节目。
8.权利要求1的方法,其中所述项目是内容。
9.权利要求1的方法,其中所述项目是产品。
10.一种用于在推荐器中向用户推荐感兴趣的项目的系统(100),包括:
存储器(120),用于存储计算机可读代码;以及
处理器(115),可操作地耦合到所述存储器,所述处理器被配置成:
使用原型简档和实地真实数据生成最初的推荐;
如果最初的推荐与实地真实数据不一致,请求关于推荐的用户反馈;
使用用户反馈生成修订推荐。
11.一种用于在推荐器中向用户推荐感兴趣的项目的系统(100),包括:
使用原型简档与实地真实数据生成最初推荐的装置;
如果最初的推荐与实地真实数据不一致,请求关于推荐的用户反馈的装置;
使用用户反馈生成修订推荐的装置。
12.一种使用推荐器向用户推荐感兴趣的项目的制品,包括:
其中嵌入了计算机可读代码装置的计算机可读介质,所述计算机可读程序代码装置包括:
使用原型简档和实地真实数据生成最初推荐的步骤;
如果最初的推荐与实地真实数据不一致,获得关于推荐的用户反馈的步骤;
使用用户反馈生成修订推荐的步骤。
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CN102884538A (zh) * | 2010-04-26 | 2013-01-16 | 微软公司 | 通过内容检测、搜索和信息聚集来丰富在线视频 |
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EP1518406A1 (en) | 2005-03-30 |
AU2003241109A1 (en) | 2003-12-31 |
US20030233655A1 (en) | 2003-12-18 |
KR20050011754A (ko) | 2005-01-29 |
JP2005530255A (ja) | 2005-10-06 |
WO2003107669A1 (en) | 2003-12-24 |
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